Objective To investigate the dosimetric effects of tumor treating fields(TTFields)patches on different radiotherapy modes for glioblastoma(GBM)patients who wear TTFields patches during radiotherapy,thereby providing dosimetric guidance for determining the appropriate radiotherapy mode.Methods With the TTFields data from GBM patients,artifact-free radiotherapy CT images were obtained utilizing 3D-printed TPU TTFields patches(3D-Print-TTFields)and anthropomorphic phantoms,and then a TTFields-synchronized radiotherapy image model was constructed.Furthermore,the treatment planning system was used to construct a dosimetric calculation model for TTFields-synchronized radiotherapy by simulating and fitting the ray attenuation rate of TTFields patches measured by accelerators.Using these models,3 kinds of radiotherapy plans were simulated and developed.Specifically,P1 simulated the conventional radiotherapy mode;P2 simulated the TTFields-combined radiotherapy mode(TTF-Com-RT),in which patients underwent radiotherapy using the P1 plan while wearing TTFields patches;and P3 simulated the TTFields-synchronized radiotherapy(TTF-Syn-RT)mode where the TTFields patches were worn throughout the entire radiotherapy process.The paired t-test was used to analyze dosimetric parameters such as target dose(D95),average scalp dose(D-skin),conformity index(CI)and homogeneity index(HI)in 3 plans(P1,P2,and P3),as well as the D95 and D-skin parameters for intensity-modulated radiotherapy(IMRT)and volumetric modulated arc therapy(VMAT)techniques in the P3 plan.Results The D95 simulated by P2 decreased by 1.35%as compared with P1(P<0.05),and the D95 simulated by P3 was 1.31%higher than that in P2(P<0.05).Compared with P1,P2 and P3 increased the D-skin by 12.56%and 14.30%,respectively(P<0.05),and the D-skin simulated by P3 increased by 1.55%as compared with P2(P<0.05).However,there were trivial differences in D95 between P3 and P1,CI and HI among all plans,D95 and D-skin between IMRT and VMAT techniques in P3 plan(P>0.05).Conclusion Based on GBM patient data,CT simulation images obtained from 3D-Print-TTFields combined with anthropomorphic phantom are artifact-free and meet radiotherapy requirements.The target and scalp dose differences between TTF-Com-RT and TTF-Syn-RT are less than 2%,and the dosimetric difference of TTF-Syn-RT using IMRT/VMAT techniques is insignificant.Therefore,clinicians can choose radiotherapy modes and techniques according to actual needs.

Objective To investigate the therapeutic effects and mechanisms of Huangqi Jianzhong Decoction(HJD)on gastric mucosal injury induced by Helicobacter pylori(Hp)infection.Methods A Wistar rat model of Hp-induced gastric mucosal injury was established.Successfully modeled rats were randomly divided into five groups:model group,low-,medium-and high-dose HJD groups,and quadruple therapy group(Omeprazole+Amoxicillin+Clarithromycin+Colloidal Bismuth pectin),8 rats per group,with an additional normal group.After 4 weeks of treatment,gastric mucosal pathological changes were assessed by hematoxylin-eosin(HE)staining.Serum levels of interleukin 1β(IL-1β),interleukin 18(IL-18),tumor necrosis factor α(TNF-α),chemokine(C-X-C motif)ligand 1(CXCL1),chemokine(C-X-C motif)ligand 9(CXCL9),and gastrin-17(G-17)were measured by enzyme-linked immunosorbent assay(ELISA).Protein expression levels of IRF8,IFN-γ,interferon-induced tetratricopeptide repeat protein 3(Ifit3)and uridine phosphorylase 1(Upp1)in gastric mucosal tissues were detected by Western Blot.Results Compared with the normal group,the model group exhibited significant gastric mucosal damage,serum levels of IL-1β,IL-18,TNF-α,CXCL1 and CXCL9 were significantly increased,G-17 was drcreased,and protein expression levels of IRF8,IFN-γ,Ifit3 and Upp1 in gastric mucosa were significantly increased,the differences being statistically significant(P＜0.05).Compared with the model group,the gastric mucosal tissue injury of rats in the medium-and high-dose of HJD groups and the quadruple therapy group was significantly improved,the levels of IL-1β,IL-18,TNF-α,CXCL1 and CXCL9 in serum were significantly decreased,G-17 was significantly increased,and the protein expression levels of IRF8,IFN-γ,Ifit3 and Upp1 in gastric mucosa were significantly decreased,the differences being statistically significant(P＜0.05).The effect in above indexes in high-does HJD groups was superior to the low-and mediun-groups.Conclusion HJD alleviates Hp-induced gastric mucosal injury by inhibiting the IRF8/IFN-γ signaling pathway and subsequent inflammatory responses.

Objective:To develop an artificial intelligence (AI)-based automatic scoring model for magnetic resonance imaging-detected extramural vascular invasion (AI-mrEMVI) and evaluate its performance and prognostic value in patients with rectal cancer.Methods:In this multicenter retrospective cohort study, a total of 2 501 rectal cancer patients from seven centers between November 2012 and December 2020 were included and divided into completely independent training ( n=1 830) and validation ( n=671) cohorts. A nnUNet-based AI-mrEMVI scoring model was constructed. Manual mrEMVI scores assigned by two radiologists served as the reference standard for accessing the accuracy of the AI-mrEMVI scoring. Kaplan-Meier survival analysis and Cox regression were used to evaluate the prognostic stratification ability of the AI-mrEMVI scores. The concordance index (C-index) was calculated to evaluate prognostic performance. Results:In the validation cohort, the manual mrEMVI scores were 0-2 in 425 patients (63.3%), 3 in 89 (13.4%), and 4 in 157 (23.4%). The AI-mrEMVI model identified 0-2 in 375 patients (55.9%), 3 in 95 (14.2%), and 4 in 201 (30.0%), with an overall accuracy of 81.1% (544/671, 95% CI 77.9%-84.0%). The 3-year disease-free survival (DFS) rates for patients with AI-mrEMVI scores of 0-2, 3, and 4 were 85.2%, 70.0%, and 58.2%, respectively, and the 5-year overall survival (OS) rates were 87.2%, 81.6%, and 62.6%, respectively (DFS: χ2=48.74, P<0.001; OS: χ2=30.04, P<0.001). Multivariable Cox regression showed that for DFS, AI-mrEMVI scores of 3 and 4 were associated with hazard ratios ( HR) of 1.75 (95% CI 1.11-2.77, P=0.016) and 2.65 (95% CI 1.86-3.78, P<0.001), respectively. For OS, an AI-mrEMVI score of 4 was associated with an HR of 2.56 (95% CI 1.62-4.03, P<0.001). The C-index values of the AI-mrEMVI scoring model for predicting DFS and OS were 0.647 (95% CI 0.608-0.686) and 0.650 (95% CI 0.598-0.702), respectively. Conclusion:The proposed AI-mrEMVI automatic scoring model demonstrated high diagnostic accuracy and performed favorably in predicting DFS and OS prognostic risk in patients with rectal cancer.

Objective:To explore the level of triglyceride-glucose (TyG) index and its effect and clinical value in predicting breast cancer.Methods:A retrospective case-control study was conducted. A total of 700 patients with breast tumors who underwent surgical treatment in the Inner Mongolia Autonomous Region People's Hospital from July 2021 to June 2024 were selected, and finally 625 patients were included, including 388 patients with benign breast tumors and 237 patients with breast cancer, and all lesions were determined to be benign or malignant by pathology after surgery. Triglyceride and blood glucose data were collected to calculate the TyG index. The correlation between TyG index and occurrence of breast cancer was analyzed by univariate and multivariate binary logistic regression models. With postoperative pathological diagnosis as the gold standard, the receiver operating characteristic (ROC) curve was used to evaluate the efficacy of TyG index in distinguishing breast cancer from benign breast tumors.Results:The age of patients in the breast cancer group and the benign breast tumor group was (57±12) years old and (42±12) years old, respectively. There were statistically significant differences in the distributions of patients between the two groups in terms of nationality, marital status, height, body mass, body mass index (BMI), cholesterol, low-density lipoprotein cholesterol, triglyceride, blood glucose levels, and color ultrasound Breast Imaging Report and Data System (BI-RADS) classification (all P < 0.05), and there was no statistically significant difference in the level of high-density lipoprotein cholesterol between the two groups ( P > 0.05); the TyG index in the breast cancer group was higher than that in the benign breast tumor group (8.6±0.6 vs. 8.4±0.6), and the difference was statistically significant ( P <0.001). Logistic regression analysis showed that in the model with only the TyG index as a variable, the risk of breast cancer increased with the increase of TyG index [ OR = 1.97, 95% CI: 1.48-2.63, P < 0.001, Hosmer-Lemeshow (HL) test P = 0.077]; in the model with 6 variables including TyG index, nationality, marriage, height, body mass and BMI, the risk of breast cancer increased with the increase of TyG index ( OR = 1.47, 95% CI: 1.08-2.00, P = 0.015, HL test P = 0.832); however, after adding age and BI-RADS classification variables, although the TyG index was still an independent influencing factor for the occurrence of breast cancer ( OR = 0.58, 95% CI: 0.35-0.96, P = 0.033, HL test P = 0.165), the rise of TyG index was an independent protective factor for the occurrence of breast cancer; in the two variable models including TyG index and BI-RADS classification, the TyG index was not an independent influencing factor for the occurrence of breast cancer ( OR = 1.10, 95% CI: 0.72-1.70, P = 0.659), suggesting that the TyG index may not be able to assist in the diagnosis of breast cancer by ultrasound. ROC curve analysis showed that the area under the curve to distinguish breast cancer and benign breast tumor according to the TyG index was 0.623, the optimal critical value of the TyG index was 8.51, the corresponding sensitivity was 59.5%, and the specificity was 62.4%. Conclusions:The TyG index of breast cancer patients is higher than that of benign breast tumor patients. Breast tumor patients with elevated TyG index have an increased risk of breast cancer, which may be a potential non-invasive biomarker of breast cancer.

Breast cancer is the most common type of cancer among women. Although chemotherapy has extended the survival time of patients with breast cancer, the cardiovascular complications caused by chemotherapy drugs are on the rise, which limits the clinical use of anthracycline drugs. At present, it is believed that the mechanisms of cardiac adverse reactions induced by anthracycline drugs may be related with oxidative stress, topoisomerase inhibitor 2, abnormal expression of non-coded RNA in cells, induction of autophagy and apoptosis in myocardial cells, and other various cell death pathways. This article reviews the relationship between insulin-like growth factor receptor 2 and cardiac adverse reactions induced by anthracycline drugs.

Strengthening the cultivation of innovation ability is the new requirement put forward by the state for higher educa-tion.High-quality curriculum design is the primary means of achieving high-quality talent cultivation.By constructing the"disease case library"and"problem graph"of immune system and related diseases,and adopting the teaching method of"combining large and small cases and integrating online and offline",this study not only consolidates students'basic knowledge,but also builds a bridge for students from theory to practice,from knowledge accumulation to creation and application.It further exercises students'ability to dis-cover,analyze and solve problems,and enhances students'innovation awareness and ability.

Strengthening the cultivation of innovation ability is the new requirement put forward by the state for higher educa-tion.High-quality curriculum design is the primary means of achieving high-quality talent cultivation.By constructing the"disease case library"and"problem graph"of immune system and related diseases,and adopting the teaching method of"combining large and small cases and integrating online and offline",this study not only consolidates students'basic knowledge,but also builds a bridge for students from theory to practice,from knowledge accumulation to creation and application.It further exercises students'ability to dis-cover,analyze and solve problems,and enhances students'innovation awareness and ability.

Objective To investigate the dosimetric effects of tumor treating fields(TTFields)patches on different radiotherapy modes for glioblastoma(GBM)patients who wear TTFields patches during radiotherapy,thereby providing dosimetric guidance for determining the appropriate radiotherapy mode.Methods With the TTFields data from GBM patients,artifact-free radiotherapy CT images were obtained utilizing 3D-printed TPU TTFields patches(3D-Print-TTFields)and anthropomorphic phantoms,and then a TTFields-synchronized radiotherapy image model was constructed.Furthermore,the treatment planning system was used to construct a dosimetric calculation model for TTFields-synchronized radiotherapy by simulating and fitting the ray attenuation rate of TTFields patches measured by accelerators.Using these models,3 kinds of radiotherapy plans were simulated and developed.Specifically,P1 simulated the conventional radiotherapy mode;P2 simulated the TTFields-combined radiotherapy mode(TTF-Com-RT),in which patients underwent radiotherapy using the P1 plan while wearing TTFields patches;and P3 simulated the TTFields-synchronized radiotherapy(TTF-Syn-RT)mode where the TTFields patches were worn throughout the entire radiotherapy process.The paired t-test was used to analyze dosimetric parameters such as target dose(D95),average scalp dose(D-skin),conformity index(CI)and homogeneity index(HI)in 3 plans(P1,P2,and P3),as well as the D95 and D-skin parameters for intensity-modulated radiotherapy(IMRT)and volumetric modulated arc therapy(VMAT)techniques in the P3 plan.Results The D95 simulated by P2 decreased by 1.35%as compared with P1(P<0.05),and the D95 simulated by P3 was 1.31%higher than that in P2(P<0.05).Compared with P1,P2 and P3 increased the D-skin by 12.56%and 14.30%,respectively(P<0.05),and the D-skin simulated by P3 increased by 1.55%as compared with P2(P<0.05).However,there were trivial differences in D95 between P3 and P1,CI and HI among all plans,D95 and D-skin between IMRT and VMAT techniques in P3 plan(P>0.05).Conclusion Based on GBM patient data,CT simulation images obtained from 3D-Print-TTFields combined with anthropomorphic phantom are artifact-free and meet radiotherapy requirements.The target and scalp dose differences between TTF-Com-RT and TTF-Syn-RT are less than 2%,and the dosimetric difference of TTF-Syn-RT using IMRT/VMAT techniques is insignificant.Therefore,clinicians can choose radiotherapy modes and techniques according to actual needs.

Objective:To develop an artificial intelligence (AI)-based automatic scoring model for magnetic resonance imaging-detected extramural vascular invasion (AI-mrEMVI) and evaluate its performance and prognostic value in patients with rectal cancer.Methods:In this multicenter retrospective cohort study, a total of 2 501 rectal cancer patients from seven centers between November 2012 and December 2020 were included and divided into completely independent training ( n=1 830) and validation ( n=671) cohorts. A nnUNet-based AI-mrEMVI scoring model was constructed. Manual mrEMVI scores assigned by two radiologists served as the reference standard for accessing the accuracy of the AI-mrEMVI scoring. Kaplan-Meier survival analysis and Cox regression were used to evaluate the prognostic stratification ability of the AI-mrEMVI scores. The concordance index (C-index) was calculated to evaluate prognostic performance. Results:In the validation cohort, the manual mrEMVI scores were 0-2 in 425 patients (63.3%), 3 in 89 (13.4%), and 4 in 157 (23.4%). The AI-mrEMVI model identified 0-2 in 375 patients (55.9%), 3 in 95 (14.2%), and 4 in 201 (30.0%), with an overall accuracy of 81.1% (544/671, 95% CI 77.9%-84.0%). The 3-year disease-free survival (DFS) rates for patients with AI-mrEMVI scores of 0-2, 3, and 4 were 85.2%, 70.0%, and 58.2%, respectively, and the 5-year overall survival (OS) rates were 87.2%, 81.6%, and 62.6%, respectively (DFS: χ2=48.74, P<0.001; OS: χ2=30.04, P<0.001). Multivariable Cox regression showed that for DFS, AI-mrEMVI scores of 3 and 4 were associated with hazard ratios ( HR) of 1.75 (95% CI 1.11-2.77, P=0.016) and 2.65 (95% CI 1.86-3.78, P<0.001), respectively. For OS, an AI-mrEMVI score of 4 was associated with an HR of 2.56 (95% CI 1.62-4.03, P<0.001). The C-index values of the AI-mrEMVI scoring model for predicting DFS and OS were 0.647 (95% CI 0.608-0.686) and 0.650 (95% CI 0.598-0.702), respectively. Conclusion:The proposed AI-mrEMVI automatic scoring model demonstrated high diagnostic accuracy and performed favorably in predicting DFS and OS prognostic risk in patients with rectal cancer.

Objective:To explore the level of triglyceride-glucose (TyG) index and its effect and clinical value in predicting breast cancer.Methods:A retrospective case-control study was conducted. A total of 700 patients with breast tumors who underwent surgical treatment in the Inner Mongolia Autonomous Region People's Hospital from July 2021 to June 2024 were selected, and finally 625 patients were included, including 388 patients with benign breast tumors and 237 patients with breast cancer, and all lesions were determined to be benign or malignant by pathology after surgery. Triglyceride and blood glucose data were collected to calculate the TyG index. The correlation between TyG index and occurrence of breast cancer was analyzed by univariate and multivariate binary logistic regression models. With postoperative pathological diagnosis as the gold standard, the receiver operating characteristic (ROC) curve was used to evaluate the efficacy of TyG index in distinguishing breast cancer from benign breast tumors.Results:The age of patients in the breast cancer group and the benign breast tumor group was (57±12) years old and (42±12) years old, respectively. There were statistically significant differences in the distributions of patients between the two groups in terms of nationality, marital status, height, body mass, body mass index (BMI), cholesterol, low-density lipoprotein cholesterol, triglyceride, blood glucose levels, and color ultrasound Breast Imaging Report and Data System (BI-RADS) classification (all P < 0.05), and there was no statistically significant difference in the level of high-density lipoprotein cholesterol between the two groups ( P > 0.05); the TyG index in the breast cancer group was higher than that in the benign breast tumor group (8.6±0.6 vs. 8.4±0.6), and the difference was statistically significant ( P <0.001). Logistic regression analysis showed that in the model with only the TyG index as a variable, the risk of breast cancer increased with the increase of TyG index [ OR = 1.97, 95% CI: 1.48-2.63, P < 0.001, Hosmer-Lemeshow (HL) test P = 0.077]; in the model with 6 variables including TyG index, nationality, marriage, height, body mass and BMI, the risk of breast cancer increased with the increase of TyG index ( OR = 1.47, 95% CI: 1.08-2.00, P = 0.015, HL test P = 0.832); however, after adding age and BI-RADS classification variables, although the TyG index was still an independent influencing factor for the occurrence of breast cancer ( OR = 0.58, 95% CI: 0.35-0.96, P = 0.033, HL test P = 0.165), the rise of TyG index was an independent protective factor for the occurrence of breast cancer; in the two variable models including TyG index and BI-RADS classification, the TyG index was not an independent influencing factor for the occurrence of breast cancer ( OR = 1.10, 95% CI: 0.72-1.70, P = 0.659), suggesting that the TyG index may not be able to assist in the diagnosis of breast cancer by ultrasound. ROC curve analysis showed that the area under the curve to distinguish breast cancer and benign breast tumor according to the TyG index was 0.623, the optimal critical value of the TyG index was 8.51, the corresponding sensitivity was 59.5%, and the specificity was 62.4%. Conclusions:The TyG index of breast cancer patients is higher than that of benign breast tumor patients. Breast tumor patients with elevated TyG index have an increased risk of breast cancer, which may be a potential non-invasive biomarker of breast cancer.